computer assisted education

What is Computer Assisted Learning? Definition, Types, Benefits and Cons

A crucial component within the realm of EdTech is computer-assisted learning (CAL). While CAL has a history spanning decades, its widespread adoption and transformative impact on education have become more pronounced in recent times. In many aspects, it has ushered in a revolution in the field of education.

Anastasiia Dyshkant

Content Marketing Manager

Technology has left its mark on every sector, bringing about positive changes. Education, in particular, stands out as a field significantly transformed by technological advancements. The amalgamation of teaching, learning, and technology is commonly referred to as EdTech.

What Is Computer-Assisted Learning (CAL)?

Before determining whether Computer-assisted Learning (CAL) is the right teaching methodology for you, let's delve into what CAL entails.

Computer-assisted Learning comprises a diverse range of technologies and concepts. The Intense School, specializing in computer and information technology, succinctly defines it as "the utilization of electronic devices/computers to deliver educational instruction and facilitate learning."

On a broader scale, CAL incorporates the use of electronic devices like CD and MP3 players (or, in the 1960s, record players), DVD players, tablets, smartphones, and television. These tools serve to enhance the teacher's communication of concepts or increase student engagement.

CAL also encompasses online courses and supplementary course materials employed in colleges, homeschooling, and distance learning. Essentially, any form of technology that contributes to the learning process likely falls under the expansive umbrella of CAL.

Key Characteristics of CAL

Computer-assisted Learning (CAL) encompasses a set of key characteristics that define its nature and impact on education. These characteristics highlight the unique attributes and functionalities that make CAL a distinctive approach to learning. Here are the key characteristics of CAL:

Technological Integration

CAL involves the seamless integration of electronic devices, computers, and digital technologies into the educational process, fundamentally changing the way students access and engage with learning materials.

Multimedia Elements

CAL incorporates diverse multimedia lessons elements, such as audio, video, interactive simulations, and graphics, to present educational content in a rich and engaging manner, catering to various learning styles.

Interactivity

Interactivity is a hallmark of CAL, allowing learners to actively participate in the learning process. Interactive exercises, simulations, and games enhance level of engagement and deepen understanding.

Self-paced Learning

CAL allows learners to progress at their own pace, providing opportunities for self-directed learning. This flexibility accommodates different learning speeds and preferences.

Data-driven Insights

CAL platforms generate data on assessment of student performance and engagement among students. Educators can use these insights to assess progress, identify challenges, and adapt instructional strategies for more effective teaching.

Collaborative Learning Opportunities

Many CAL tools incorporate features that promote collaboration among students. Online forums, group projects, and virtual discussions facilitate interaction and shared learning experiences.

Real-time Feedback

CAL often provides instantaneous feedback to learners, allowing them to assess their performance immediately. This timely feedback promotes a continuous learning loop and helps students address areas of improvement promptly.

Inclusive Learning

CAL has the potential to make education more inclusive by providing access to learning resources for individuals with diverse needs, including those with different learning abilities and preferences.

Types of CAL

Tutorials within the realm of CAL serve as comprehensive guides, offering learners step-by-step instructions and in-depth content to enhance their understanding of specific subjects. These instructional modules provide a structured approach to learning, guiding students through complex concepts with clarity and precision.

Simulations

CAL incorporates simulations, dynamic environments that replicate real-world scenarios. These interactive simulations empower learners to engage in hands-on experimentation, allowing them to apply theoretical knowledge in a practical context. Simulations bridge the gap between theory and application, fostering a deeper understanding of complex concepts.

Programs for Repetitive Skill Reinforcement

Certain CAL programs focus on repetitive skill reinforcement, employing a practice-oriented approach to enhance specific abilities. Through consistent and targeted exercises, these third-party programs ensure that learners achieve mastery in a particular skill set, reinforcing knowledge through ongoing, deliberate practice.

Gamified Learning

Gamified learning represents an innovative approach within CAL, integrating elements of gameplay into educational content. By introducing game mechanics such as challenges, rewards, and competition, gamified learning transforms the educational experience into an engaging and motivating journey, capturing the interest and enthusiasm of bored students.

Multimedia Learning

In CAL, multimedia learning leverages a diverse range of media, including audio, video, and interactive elements, to convey information in a rich and varied manner. This approach recognizes and caters to different learning preferences, providing a holistic and immersive educational experience that goes beyond traditional text-based instruction.

Intelligent Tutoring Systems (ITS)

Intelligent Tutoring Systems (ITS) represent a sophisticated facet of CAL, employing artificial intelligence to deliver personalized and adaptive tutoring. These systems analyze individual learning styles and progress, tailoring the educational content to the unique needs of each student. ITS enhances the effectiveness of instruction by providing targeted guidance and support.

Virtual Labs

Within CAL, virtual labs create digital environments that replicate the conditions of physical laboratories. These simulations enable learners to conduct experiments and explore scientific concepts in a virtual space, offering a safe and accessible platform for hands-on learning. Virtual labs enhance practical understanding and experimentation in diverse fields.

Collaborative Learning Platforms

CAL embraces collaborative learning platforms that facilitate group interaction and cooperation among students. These platforms promote shared learning experiences, encouraging students to collaborate on projects, discuss ideas, and collectively solve problems. Collaborative learning platforms foster a sense of community and engagement within virtual educational spaces.

Mobile Learning (mLearning)

Mobile Learning (mLearning) is a dynamic aspect of CAL, leveraging the ubiquity of mobile devices to deliver educational content on the go. This flexible approach enables learners to access information anytime, anywhere, breaking down traditional barriers to learning. mLearning enhances accessibility and accommodates the diverse lifestyles of modern students.

Interactive Whiteboards

Interactive Whiteboards within CAL provide a digital canvas for educators to create engaging and interactive presentations. These tools enable individual teachers to incorporate multimedia elements, annotate content, and encourage student participation in real-time. Interactive Whiteboards enhance the overall teaching and learning experience by fostering dynamic and visually compelling educational interactions

Learning Management Systems (LMS)

Learning Management Systems (LMS) represent central hubs within CAL for organizing, delivering, and tracking educational content. These platforms streamline the management of learning resources, providing educators with tools to structure courses, assess student progress, and facilitate communication. LMS enhances the efficiency of educational administration in both traditional and online settings.

Adaptive Learning Systems

Adaptive Learning Systems within CAL offer a personalized approach to education by dynamically adjusting content and pace based on individual learner progress. These systems use data-driven insights to tailor the learning experience to each student's strengths, weaknesses, and preferences. Adaptive Learning Systems optimize stronger learning outcomes by providing targeted support and challenges.

Augmented Reality (AR) and Virtual Reality (VR)

The integration of Augmented Reality (AR) and Virtual Reality (VR) technologies in CAL creates immersive visual learning experiences. These technologies transport students into three-dimensional environments, allowing them to interact with content in ways that transcend traditional methods. AR and VR enhance engagement and understanding by providing a sensory-rich and interactive educational journey.

Computer-Mediated Communication (CMC)

Computer-Mediated Communication (CMC) is an integral component of computer assisted strategy that facilitates interaction and collaboration among learners through digital channels. In virtual learning environments, CMC enables students to communicate, share ideas, and collaborate on projects. This mode of communication enhances the social aspect of learning, fostering a sense of community in the digital educational landscape

 Advantages of Computer-assisted Learning

Flexibility and Accessibility

Benefits of computer assisted learning provide learners with the flexibility to engage with educational content at their own pace and convenience, breaking away from traditional classroom schedules and catering to various learning styles.

Personalized Learning

Intelligent algorithms and adaptive technologies in CAL systems tailor content and instruction to the individual needs and progress of each learner, fostering a personalized and effective learning experience.

Interactive and Engaging Environments

Incorporating multimedia elements, simulations, and gamified features in computer assistant learning captivates students' attention, making the learning process more enjoyable. Interactive platforms encourage hands-on experimentation and group participation.

Development of Digital Literacy

CAL promotes the development of digital literacy skills as students navigate online courses, use various software applications, and interact with digital content, preparing them for the demands of the modern workforce.

Disadvantages of Computer-assisted Language Learning

Limited human interaction.

CALL may limit opportunities for authentic verbal communication and cultural exchange, hindering the development of conversational language skills that thrive on interpersonal interaction.

Dependency on Technology

Technical issues, software malfunctions, or connectivity problems in computer assisted technology can impede the learning process and create frustration. Unequal access to technology may also contribute to disparities in learning opportunities.

Resistance to Technology

Traditionalists may resist CALL, viewing technology as a potential barrier to effective language learning. The absence of a physical teacher-student connection might be perceived as a drawback, particularly in language education.

Over-reliance on Standardized Content

Some CALL programs may adopt a one-size-fits-all approach, making it challenging to tailor language learning to individual needs and preferences. This may not adequately address diverse learning styles and goals.

CAL in Different Education Levels

Cal in primary education.

Computer-assisted Learning (CAL) in primary education plays a crucial role in laying the foundation for a child's learning journey. In this setting, CAL is often utilized to introduce fundamental concepts in subjects like mathematics, language arts, and science. Interactive and engaging educational games, tutorials, and multimedia content cater to the diverse learning styles of young learners. CAL in primary education aims to make the learning process enjoyable, fostering a positive attitude toward education from the early stages.

CAL in Secondary Education

In secondary education, computer assisted education becomes more sophisticated, aligning with the advanced academic requirements of students. CAL tools are integrated into the curriculum to supplement traditional teaching methods. Virtual labs, simulations, and interactive platforms are employed to provide hands-on experiences and deepen understanding in subjects such as biology, physics, and chemistry. Additionally, CAL in secondary education often includes online resources, collaborative learning platforms, and adaptive technologies to address the diverse needs and interests of students during their formative years.

CAL in Higher Education

At the higher education level, CAL takes on a more advanced and specialized role. It becomes an integral part of various disciplines, offering online courses, virtual labs, and multimedia resources that complement traditional lectures. CAL in higher education facilitates self-directed learning, allowing students to access lesson materials at their own pace. Learning Management Systems (LMS) are commonly used to organize course content, track progress, and facilitate communication. Moreover, in professional fields, computer assisted technology is utilized for skills training, providing realistic simulations and scenarios to prepare students for real-world challenges.

Examples of Successful CAL

Khan Academy

Khan Academy is a widely acclaimed online platform offering a vast array of free educational resources. Known for its clear and concise video tutorials, Khan Academy has successfully utilized CAL to make subjects like mathematics, science, and coding more accessible to learners worldwide.

Duolingo has revolutionized language learning through its gamified approach. By integrating CAL elements such as interactive exercises, quizzes, and real-time feedback, Duolingo has successfully engaged users in language acquisition, making it one of the most popular language learning apps globally.

Coursera is a prominent example of computer assisted education at the higher education level. Offering a wide range of online courses and specializations from renowned universities and institutions, Coursera has made higher education accessible to a global audience. Its adaptive learning features cater to diverse learning styles.

Google Classroom

Google Classroom is a widely used Learning Management System (LMS) that incorporates CAL features. It enables teachers to organize assignments, provide feedback, and engage students through collaborative tools. Google Classroom streamlines the educational process, fostering effective communication and digital collaboration.

These examples illustrate the diverse applications of computer assistant learning across different educational contexts, showcasing its effectiveness in making learning more engaging, accessible, and tailored to individual needs.

In conclusion, Computer-assisted Learning (CAL) represents a transformative force in the realm of education, reshaping traditional teaching methodologies and expanding the horizons of learning possibilities. As we've explored the definition, various types, and the associated benefits and disadvantages of CAL, it becomes evident that its impact is multifaceted.

The versatility of CAL is exemplified in its ability to adapt to different education levels, from primary education to higher learning, catering to the specific needs and developmental stages of learners. Successful implementations, such as Khan Academy, Duolingo, and interactive tools like SMART Boards, underscore the efficacy of CAL in making education more accessible, engaging, and tailored to individual learning styles.

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The future of computer-aided education

Chris Piech is a professor of computer science who studies how computers can help students learn.

In comparing human- and computer-aided education, he says humans are great one-on-one, but AI is more consistent at grading and feedback. He and colleagues have created several generative AI grading apps to take advantage of these relative strengths, as he tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

Listen on your favorite podcast platform:

Related : Chris Piech , assistant professor (teaching) of computer science

[00:00:00] Chris Piech: How do you get joy in education? I think there's plenty of kids in K-12 education. There's plenty of adults who need to get retrained, uh, and they're not necessarily finding the experience to be that motivating or joyful. Um, and part of my job is to come up with tools and things that can bring that joy into education.

[00:00:17] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host Russ Altman. If you enjoy The Future of Everything, please hit follow in the app that you're listening in. This will guarantee that you never miss an episode. 

[00:00:34] Today, Professor Chris Piech from Stanford University will tell us how he's training computers to help students learn better and to help teachers teach better. It's the future of computer aided education. 

[00:00:47] Before we get started, please remember to follow the show to ensure that you get alerted to all of our new episodes and never miss an episode on the future of anything. 

[00:00:56] Computers are everywhere in our lives. But one of the places we don't think about them as much is in the school room. We have teachers. We look up to our teachers. They teach us stuff. Then they give us grades. That's how we've always done it. Well, guess what? Things are changing. We're starting to see sophisticated computer programs that actually make a difference in education. What do I mean? They make students learn better. The students may even enjoy the experience more. And they helps teachers think about how they're going to present information and with grading and assessment. 

[00:01:35] Well, Chris Piech is a professor of computer science at Stanford University and an expert on using AI and computers to teach. He focuses a little bit on teaching computer science, his chosen area, but he's had a wide array of applications that may surprise you. 

[00:01:50] Chris, you focus on teaching people how to program, how to do computer coding, uh, with computer assistance. For people who haven't ever coded, can you just define what that's like and why it's challenging?

[00:02:04] Chris Piech: Yeah. A good way to describe it could be, imagine you want to give instructions to a computer, maybe you want it to open a file, read through and then do some work for you, or maybe you want to make a beautiful game. All of that, everything you experience on your computer, on your phone. It's all been created by a person who's written some code.

[00:02:22] Now, the experience of writing code is maybe surprisingly mundane. It's a little bit like opening up a Google doc and writing down your instructions. You need to learn to speak the language of these instructions. And that's what I teach in my class. 

[00:02:35] Russ Altman: Okay. So that's what we're trying to get to. And now I know that you are, you're committed to trying to understand how we can kind of use computers to improve that experience. That will include AI and I'm sure we'll get to AI, but what is, what's the problem that we're trying to solve here? Are there not enough coders who know how to teach? Are we unable to create the environment for people to learn how to code? What's the problem? 

[00:02:59] Chris Piech: Yeah, so, um, you know, I'll actually broaden it a little bit in that I love teaching, like, I think I was one of those people who was just born to teach. It's just brings me so much joy. I would probably teach anything. I just happen to love coding as well. But I think a lot of the challenges I think are more broad.

[00:03:13] It's like, what are the challenges in learning? And if I had to name one, I think motivation is a pretty big one. Like, how do you get joy in education? I think there's plenty of kids in K-12 education. There's plenty of adults who need to get retrained, uh, and they're not necessarily finding the experience to be that motivating or joyful. Um, and part of my job is to come up with tools and things that can bring that joy into education. 

[00:03:37] Russ Altman: Okay. So let me just ask, um, it is not my instinct, when I think about setting up a joyful learning experience, to do, to go to a computer screen and interact with a computer, right? I think about a group of people, maybe a teacher who is inspiring, who relates to me and also gets me thinking about how I can improve myself and learn stuff. So tell me how this turns into, we should make computers better at teaching. 

[00:04:05] Chris Piech: Yeah, so actually, um, the question is really well posed, and I feel like, uh, I approach it from a similar perspective. You know, I'm an old man now. I've done a lot of experimentations. 

[00:04:13] Russ Altman: You have no idea, but we'll get to that later.

[00:04:17] Chris Piech: Um, you know, the single thing that I've seen have the biggest impact on learners is that relationship building with a teacher. Um, and I think we can all appreciate and relate to that. Um, so surprisingly, uh, the, or when, or maybe not surprisingly, but when the pandemic came and I had my chance to try and build my own version of joyful education, I had all the AI tools at my disposal.

[00:04:41] I sit in the AI lab at Stanford, um, but I actually turned to a rather simple idea that I think might really be important for education, uh, which is, people who have recently learned are actually can be remarkable teachers if they're set up properly. So, you know, when I had my chance, I set up a classroom with ten thousand students, but a thousand teachers. Uh, the teachers were all just a little bit beyond the students and they're all leading a group of ten. I think learning's so relationship driven. 

[00:05:12] Russ Altman: I, you know, I love that because I tell, when I'm giving kind of mentorship to new teachers, and I've been teaching for a long time as well, I say to them, you don't have to know everything. You just have to be about an hour ahead of all the students that you're about to teach, right?

[00:05:27] Because the class will be an hour. And so if I can get them to everything that I know, they'll never know that the next minute I would have been clueless. 

[00:05:35] Chris Piech: Yeah. And, you know, to play it really safe, we get teachers who are about six weeks ahead of a student but. 

[00:05:40] Russ Altman: But so that's very powerful. And how does that work?

[00:05:43] So there's a whole bunch of things there. You, first of all, you have a whole range of students coming in with, at different levels of skill and knowledge, um, these teachers, um, who are recent enthusiasts and recent acquirers of knowledge. Um, I, it's easy to believe that they're enthusiastic, but how do you set it up for their success?

[00:06:04] Chris Piech: Yeah, you know, there's, the program called Code in Place is a Stanford program and folks are welcome to join if they find it interesting. Um, so the way we set it up is we make sure that the teachers have good training. Uh, we kind of have an application process and we select for who we think is ready to take that step.

[00:06:24] Uh, and here's one of the interesting things. By this point, Russ, we've had about four thousand teachers. Um, and in that experience, we've really gotten to learn what makes for a good teacher and you would be surprised how great amateurs can be. Um, you know, there's one way of talking about it is, if they can learn to be humble and not overstate what they know, they have this real advantage, which is that they know the struggle. They know what it's like. 

[00:06:50] Russ Altman: And it's been recent. 

[00:06:52] Chris Piech: Yeah. Um, and so, you know, as I said, we're learning a lot about what makes for great teaching. And certainly one of the big findings is we were really underestimating how great amateurs could be. 

[00:07:02] Russ Altman: So you really gave a great answer because I was asking about how are we going to have those inspiring people in front of us? And even if it's through a screen or through a Zoom, it sounds like you've addressed that. And that's part of the plan. 

[00:07:14] Let's zoom out. I want to ask about the status of automatic coding and AI grading, and there's just so many topics that your work touches upon. I guess, I just want to, for people who don't, aren't aware of it, uh, computer programming has kind of been revolutionized even among professionals in the last couple of years because of AI.

[00:07:34] And could you just describe for us what the status is? 'Cause I'm sure that impacts the ways in which you think about how to get a new coder, uh, into the field. 

[00:07:43] Chris Piech: Yeah. So for those of you guys, um, who are interested, there's this big revolution that's happened in the last few years, uh, they maybe could call it the generative AI revolution. Uh, basically realized that we throw insane amount of computer power at a thing called a neural network, it can start to do crazy things like produce language. And you might have interfaced with something like ChatGPT or heard about it. What you might not know is that ChatGPT isn't just able to write text or poems about pirates, it can also write Python and computer code.

[00:08:16] Uh, Python is one of the languages that we program in. And I would almost argue it could be, I would say it's probably better at coding than at language. So if you've experienced these things, uh, you know, maybe a good metaphor is whatever you're thinking about its capabilities to produce fluent text, um, you know, I would say it's doing a pretty good job of coding as well. 

[00:08:34] Russ Altman: Okay. So that is now a thing. And if I understand it even professional coders are taking advantage of the, of these tools. Um, so, 

[00:08:42] Chris Piech: Yeah, I program all the time. I use them all the time. I mean, I don't know if you need to talk about this, but I've never had more fun programming.

[00:08:48] Russ Altman: Okay. So it has not taken the joy away from programming because I, as a youth, I did a lot of programming myself and it was super fun. It was literally the only thing that could keep me up all night was a thorny, exciting programming challenge. Uh, and, um, well, that's a whole different story. Uh, so how does that change how you want to teach people the principles of coding? 

[00:09:12] It's a lot, you know, it's, I'm sure it's very analogous to the problem that English teachers are having right now about writing, is that they have, that students have access to these powerful tools. It forces you to rethink, how am I going to teach them to write an essay? And what's the equivalent question in your world? 

[00:09:28] Chris Piech: Yeah. Well, okay. So let's start by how fun programming is. And let's talk about why it's so fun. And then I think that will help answer all your questions. So programming, it's a joy if done right. And one of the reasons the joy is you're creating things out of just what is in your imagination.

[00:09:44] You're like, I dream of this particular game, and I can turn that dream into something I can share with my loved ones. And that creation is such a human experience, like you've just made something that you can give, and if a loved one says that what you made is valuable, uh, it's like the best feeling. Um, and that's one of the reasons that I'm having more fun now, is because actually these tools have elevated what I'm able to create.

[00:10:05] You know, there used to be limitations to how quickly I could read documentation and learn about new APIs and how quickly I could pick up new languages. Um, and now with these new tools, I'm just having the time of my life. And speaking on that, you brought up English teachers having to teach essays, right?

[00:10:24] As I said, I think a lot about motivation and education. Um, and I, I'll say that there's this opportunity for creation all over, and it's not just in coding. Funny story, the three-year-old and I did the funniest thing with my three year old. Of course, I want her to be able to read and write just like every other father.

[00:10:46] Uh, but also, I got bored one morning and you know what we did? We just pulled up, we pulled up these large language models and we just wrote a book. It was producing the images, um, and it was helping up with the story when we got stuck. And then once we got that book written, I just spent thirty more minutes and I got it printed with Google Photos, uh, and then I had her holding a book.

[00:11:06] Russ Altman: Oh my goodness. 

[00:11:06] Chris Piech: It was the coolest thing. You can imagine my three-year-old, like, she likes books, but to hold a book that she had helped craft, it was just a neat experience for her. 

[00:11:13] Russ Altman: And she was fully aware of being part of this creative process, it sounds like. 

[00:11:17] Chris Piech: Oh, yeah. A little bit too aware. Now, if you come to my house, it's like the first thing she'll show you, like, we're trying to, like, tone that down, but that's a different story. But you know, like, that joy is cool. Hey, so Russ, what am I getting at? 

[00:11:30] This moment for education could really be different. And if you talk to a lot of smart people, you'll hear a lot of people say this moment will be different because AI will be a great tutor. I'm not saying that's wrong, but I'm saying one of the reasons this moment will be different is not because of what AI can do, it's what humans can do. It's using these tools to expand what we can create. Could be a way to unlock making learning more fun. You could go much faster from learning the intros to I'm creating something of value. And I'm excited about that. 

[00:11:57] Russ Altman: I can't tell you how reassuring it is to me, and I'm sure lots of people, to have someone, who like you, who's working on computer aided education. And that, I know that's an old-fashioned term. To have you say those things about joy and creation and the human aspect. It is so reassuring because as a child of the nineteen sixties and seventies, um, you probably have studied as an archaeologist, the kinds of tools I was exposed to, where I remember as like a seventh grader saying, I will never sit in front of a computer and try to learn anything.

[00:12:29] And this is, you know, I became some, something of a computer professional, but they were such atrocious experiences that it probably delayed my open mindedness by at least thirty years. Okay. 

[00:12:41] Chris Piech: Do you want to hear a story on that? 

[00:12:45] Russ Altman: I do. 

[00:12:45] Chris Piech: Okay, so, you know, I'm a scientist as well. Um, and as you said, I'm a teacher, but I also, and I make tools, but I also like to do some science to see what's working, what's not working. Last year, as we mentioned, I had this big class, ten thousand students, a thousand teachers, and we actually ran two experiments. 

[00:13:04] In one experiment, we had early access to GPT4. Half the students got early access, half the students did not. We do this A B testing when we're not sure if a tool is going to help people. We ran a second experiment, going on at the same time. In this second experiment, we kind of had a breakthrough in how we could do one on one teaching. It's hard to do with ten thousand people. We don't need to get into the details, but we'd had a breakthrough. And it allowed us to do one on one teaching. We did a similar experiment where some people would get access to one-on-one teaching.

[00:13:31] And then some people would, eventually everyone gets access to everything. But at the beginning, we would like to learn what's working. Okay, I'll first tell you about the one-on-one teaching. In the one-on-one teaching, fifteen minutes with a near peer. So somebody who's like six weeks ahead of you, ten percentage point improvement in your chance of completing the material of the class, huge. 

[00:13:49] Russ Altman: Yes, yes.

[00:13:49] Chris Piech: I've never seen a result like it. That's the biggest result ever seen. So I'll invite you to think what, do you think the AI did? Did it achieve that human level of ten percentage point improvement? 

[00:14:02] Russ Altman: So this is a human who has just learned, who's enthusiastic. You already told us that you filtered them for a bunch of characteristics. I can't imagine the AI was as good. 

[00:14:15] Chris Piech: It not only wasn't as good, it has a four percentage point less likely to finish the class if you had access to GPT. So I looked at the, we looked at the conversations. They were healthy conversations, they were talking about concepts. I think you're not the only child of the sixties.

[00:14:32] There's a lot of us who, there's something about the human that I don't want us to lose. Hey, there's something about the AI that's really cool. 

[00:14:38] Russ Altman: Yes. 

[00:14:38] Chris Piech: We'll talk about how we can bring that into the future of learning, but we better not let the human part go. 

[00:14:43] Russ Altman: Good. Good. Okay. I feel validated and thank you very much. Okay. So let's go into, I know one of the areas, you've done a lot of stuff and I, you know, I Google stalked you last night. So I know about all your papers. One of the things you've looked at is assessment of students and that's getting serious, right? Because this has impacts on their future, on their, um, uh, on their job potential, on their ability to get into the next level of, uh, either education or job. So it's very sensitive and people are, I'm sure worried about it. 

[00:15:15] Tell me how you approach computational assessment of learners. How should we think about it? And I'm guessing you've done humanistic things in that direction, but I have no idea what they are.

[00:15:26] Chris Piech: No, no, it's such a good question. So like, you know, um, for a while there, one of my main quests was to help us understand humans based off their work. So you're learning physics, you're learning English, you're learning programming. While you're doing this learning, you're producing work. And, you know, I think it's a grand challenge in algorithmic education to understand you from the open-ended work. 

[00:15:48] Hey, if you're doing multiple choice, by the way, boy, can I really model what you know. It turns out I've got this algorithm that’s called Deep Knowledge Tracing. We use it in Duolingo, but, um, I don't dream of people doing multiple choice questions. I dream of people, uh, doing more complicated things. So that's the first bit. You know, no one likes assessment, but what we're able to grade is the assessments we can give.

[00:16:12] And if we can grade more complicated things, we'll be able to, as teachers, have more interesting learning experiences. So if the only thing we can grade is multiple choice, you're going to get a lot of multiple choice tests. If the only thing we can grade is you programming, you're getting a lot of programming.

[00:16:28] You know, the dream, Russ, though, is that you could be doing an open-ended project. Doing something that you really find motivating and enjoyable, and then we can give you feedback. Okay, so that's just a framework. From there we could talk a lot about the state of the art. 

[00:16:42] Russ Altman: Well, yeah, so I would like to know, um, uh, where are we, you know, I, one of the things that comes to mind and it's terrible is, and I don't even want to go there yet because we haven't talked about grading and assessment, but there's also the issue of cheating, which is kind of intimately tied in with all this.

[00:16:58] And I don't even like saying that word, right? It's an ugly word. It's an ugly idea. So maybe we'll put that aside, but how do you approach grading fairly? Uh, and what do you do about the variety of backgrounds and cultural assumptions that students come in? And then when you try to reduce all of that, either to a number between one and a hundred or a letter from A to F or whatever it is, or even a paragraph. You know, our friends at UC Santa Cruz, they write paragraphs describing how the student did. Um, how do you, how are you thinking about how to do that? And where is the state of the art? 

[00:17:33] Chris Piech: Yeah. 

[00:17:34] Russ Altman: How do you want your three-year-old to be graded when they get to first grade? 

[00:17:37] Chris Piech: Oh, I love the question. You know, um, and I'll bring the same sensitivity, like, who am I to judge anyone? You know, I'm a curious human. You're a curious human. Like, I don't presume that, I don't really like this position of power. And you mentioned how it could affect people's lives and that really makes me nervous. Um, but it's a big deal and there's a lot of nuance to it. You know, the cheating nuance is an interesting one. Uh, the effect on people's lives, interesting one, but maybe a safe place to start is actually the demographic fairness. 

[00:18:07] Russ Altman: Okay. 

[00:18:07] Chris Piech: So, uh, you know, Russ, I've actually never used, so I might be one of the few people in the world who's like spent a decade studying this. So we've got a bunch of algorithms that you could call state of the art. There's almost zero algorithms I've used for real assessment. Um, and you know why? For me, grading is a different thing. Grading is the proof that an algorithm understands students. So a lot of the algorithms I wanna make, they're gonna help students in some way. They're gonna help teachers in some way. 

[00:18:42] And the central piece is, can you understand a student? It just so happens that grading is one of the few numerical things that I've got to see if an algorithm is doing a good job understanding. Um, so I have done experiments, were we to use this grading for real assessment, would it be fair? As I said, we haven't actually used it for real assessments. Uh, and the answer was yes. Now, what was I grading? We took ten thousand students writing an exam for code in place. Now, right now the exam is, we call it a self-diagnostic. You know, there's no feedback. You just take the exam and then you're done.

[00:19:17] Just the experience is the experience. But then we thought, what if we ran an AI grader on this? And then we ran the AI grader, um, and we compared it to human graders. And you might not find this that surprising, but human graders are not all that accurate. 

[00:19:32] Russ Altman: And they're a little biased in their own special ways.

[00:19:35] Chris Piech: Yeah. Yeah. And, um, and then we can see that we were a little bit more accurate. We then actually gave it to the students and said like, hey, this is not a grade, but here's some automatic feedback if you find it interesting. And then we spliced in the human feedback and the AI feedback and the students in my class were actually preferring the AI feedback.

[00:19:50] So that just gives you an idea of what's possible. Uh, I will say that the answer probably would be different depending on the subject. I think coding, you know, we have people from a hundred and fifty countries Russ. So like, I don't, I got to find out, is it biased against Nigerians? And the answer is no, you know, coding is something like a universal language.

[00:20:09] Um, there's not too much about your demographics that leaks out when you're doing a little coding task. It could be really different if you're writing a personal essay. 

[00:20:17] Russ Altman: Yes. Yes. 

[00:20:18] Chris Piech: Um, it could be, and actually it could be really different if it's evaluating based off your resume, like all sorts of biases, you know, uh, if you're interested in these large language models that are trained off the internet, that is a wild, wild world.

[00:20:32] Russ Altman: This is The Future of Everything with Russ Altman. We'll have more with Chris Piech next.

[00:20:52] Welcome back to The Future of Everything. I'm Russ Altman, your host, and I'm speaking with Professor Chris Piech from Stanford University. 

[00:20:58] In the first segment, we talked about the promise and possibilities for using computers in education. In this next segment, we're going to talk about how can computers grade less structured information, things where there's a lot of creativity and spontaneity involved. Also, how can you use computers for other kinds of tests, not just academic tests and grading? And finally, what is this idea of generative grading? We've heard of generative AI. What's generative grading?

[00:21:26] I know that you've also done some work recently looking at feedback and assessment, and in a less structured environment. So tell us what's the state of that kind of work? 

[00:21:36] Chris Piech: Yeah. So, you know, if you're interested in giving somebody feedback, there's different degrees of difficulty. Multiple choice is the easiest. Uh, short answer is the next easiest. A step up from that is something like coding. Uh, but then there is this particular type of coding, which represents a goal we're shooting for, which is giving people feedback when they're just having fun and doing something unstructured.

[00:22:00] Um, and so this does express itself in programming and it expresses itself when people are making games or web apps, particularly ones where it's just like, hey, you go make a, a great application. Use the concepts we've learned in class. This is a really difficult grading task. We use a particularly neat idea. Have you ever seen a program learn how to play chess? 

[00:22:24] Russ Altman: Uh, I think so. Yes. 

[00:22:25] Chris Piech: Yeah. So it kind of like learns by playing. It just like plays a whole bunch of games of chess and then it gets really good at it. So what we do when we're grading, those open end things is really different than multiple choice. The way we, what we do is we make a program. Um, now it's called DreamGrader, uh, made with a bunch of wonderful colleagues. And what DreamGrader is going to do is it's, it's going to play your work. It's going to interact with it. It's going to, you know, if you made a game, it's going to move the paddle. If you made an app, it's going to try and press the buttons.

[00:22:54] Um, and through interacting with its work, that's how it's gaining its understanding of what you're doing. And it's really wonderful. See, it's like the power of these chess engines in the hands of graders. I'm excited about it. Not just technically. Okay, I'd say like nerdy. Very fun. But also as a teacher, 'cause I want to give those things. I just can't because grading them is so hard. 

[00:23:16] Russ Altman: So have you created kind of a rubric of what make, so like, give me an example of what the feedback would be. So I've played your game, like, dear Joan, Joan, I played Pong. Here's what I think of Pong. Like, tell me like how that looks.

[00:23:32] Chris Piech: Yeah. Well, okay. So right now we actually do use it in the class I'm teaching. So maybe this is the one example where I'm actually using it. Um, here's what it looks like, we have something called Breakout. It's just like Pong. Uh, you have a pad. 

[00:23:45] Russ Altman: I just guessed Pong. 

[00:23:47] Chris Piech: Yeah. Yeah. It was a good guess, fantastic. And students like to go above and beyond, they like to change the colors. Sometimes they'll add like, you know, level ups. So what the algorithm is going to do is it's going to play it. And it's going to try and make little movies. It's like, here's a moment when I was playing your game, and it did something that I thought was wonderful. Or, I was playing your game, Joan, and when I was playing it, this happened, and I'm pretty sure you didn't expect for this to happen, and that was a mistake.

[00:24:14] Russ Altman: And so, and you've been able to figure out how to do that in a very general way, so that no matter what game they throw in front of you, or is this kind of a Pong specific feedback? 

[00:24:23] Chris Piech: No. Okay, it's somewhere in between. I don't want to overstate what's possible. It's, this is still research. It's still science. Um, it's not just about Pong. And it should be anything that's interactive, but we're at the early stages of seeing where it breaks down. So when you get to really complicated things, like, I don't know if somebody programmed World of Warcraft, I don't think this thing is going to be able to play World of Warcraft and say like, that was a pretty good job, Joan. 

[00:24:44] Russ Altman: Right. Well, I, you know, I have, I'm sure you know this, but I'm just going to make it explicit. When I do research with my graduate students, it's like a game. Yes, we're doing research on biology and medicine, but on the day-to-day basis, we're writing code and we're generating graphics and some of the graphics get us excited and even giggling with like how good they look.

[00:25:06] And some of the graphics are like profoundly depressing and like indistinguishable from noise. And so as you were describing this, I'm thinking to myself, this is not so far from being able to evaluate the fun and the productivity of a research project, because it's in many ways we think of it and it can be construed as a game.

[00:25:26] Chris Piech: I mean, I love it. I've not thought about it from that perspective. 

[00:25:30] Russ Altman: All right. So we'll have to have a meeting. We'll have to have a lunch discussion. I want to get to another topic, which is you even talking about tests like for students, but you've actually generalized your work to look at other kinds of tests. So tell me about that. 

[00:25:42] Chris Piech: Yeah, you know, earlier in this, uh, conversation, we talked about how tests can be a little bit depressing, like who wants to be evaluated. But there's some tests where you just, like, really, really, really want to get the right answer. Um, and I'd say that those, the class of tests I'm talking about are medical tests. Like sometimes you go into a doctor's and you really want to get an accurate evaluation of what's going on. Now, sometimes that involves taking a photo, but sometimes it involves a human response. Uh, so let me paint you a picture, an eye test. You walk into an ophthalmologist's office, something's wrong with your eyes, and we need to know how well you can see.

[00:26:13] The only way we can know how well you can see is by asking you questions. Uh, and it turns out through decades of studying how people learn and how we can give feedback to things like coding assignments, we've actually figured out how we can get much better at giving feedback to people who are doing medical tests, like the eye test, for example.

[00:26:30] Russ Altman: Wow. So that really hits home. I recently was diagnosed with double vision and I was well aware that it was a qualitative description I was given of these symptoms and they were clearly struggling, I mean, in a good way, to understand exactly what the problem was. And so are you mostly helping the ophthalmologist or the optometrist, not so much the patient. I mean, of course it's helping the patient ultimately, but you're trying to help them make a more accurate assessment of the situation. 

[00:26:57] Chris Piech: Yeah. You know, the, we can get a more accurate reading of how well you can see in shorter time. That's both for the ophthalmologists. It was designed for people with more serious eye diseases. Like if you're just trying to find glasses, you know, not a, you probably don't need to know to incredible precision. Where it matters a little bit more is if you have like, um, a chronic eye disease that you have to track every day. So I actually made this is not that important, but I happen to have a chronic eye disease.

[00:27:23] So, and I want every day, I kind of want to know, has my vision gotten worse? And if even subtle changes in my vision can be really important for me to treat quickly. Um, so that was this case where I really wanted that high fidelity measurement, but the problem was me, like I needed to have this qualitative interpretation of how well I could see.

[00:27:42] Russ Altman: Yes. Yes. And your ability to discuss and describe on any given day, it's stressful because you don't want to miss the chance for the, uh, for the physician, for the clinician to make an appropriate inference by, and so you want to give them as good information as possible, but anything that they could have to help understand and appreciate what you're saying, I totally am with you there, and I saw that. 

[00:28:01] Okay, in the last few minutes, I want to ask you about this idea of generative grading. First of all, generative AI has been on lots of people's minds, and I don't know exactly what you mean, but it sounds like an exciting idea. So I just want to give you a chance to describe what is generative grading, and is it the future?

[00:28:19] Chris Piech: Yeah, okay, so I'm glad you asked. Generative grading is this algorithm that we made in the lab, and it's kind of near and dear to our hearts because, um, both it's had great impact, but also we like the ideas behind it. Uh, we all know that generative AI is impacting folks in, in lots of ways. Uh, and it, one of the hard tasks for a teacher is to grade.

[00:28:41] And there is this open question of how we can make this work useful to a teacher. Um, there is something really special about generative grading that's a little bit different than your classic large language model. So a large language model is, that's the neural network behind a lot of things like OpenAI.

[00:28:58] What's different about the way we do it is, the insight came from this. If you want to grade open ended work, it is much easier to generate an example of a student with a misconception than it is to take broken work and guess what the misconception was. So if I tell you you're a teacher, and I say, a student doesn't understand a for loop, and they tried to do this assignment, what could their work look like?

[00:29:25] It turns out, teachers find this much easier. Uh, and instead I say like, here's a broken program, what don't they understand? You have to guess through every single decision they could have made, every thought they could have had. That inference task is just insanely difficult. So we have this idea, kind of, even before large language models came out, that generative thinking is much easier and could be really important for grading.

[00:29:48] So now in the modern world, the way we employ this is in two ways. One, you can imagine artificial intelligence can be very helpful for generating the same sort of thought process, but you know what's great? Turns out humans are still amazing at it as well. Not only do we invite AI to think about generative stories of how students can go from misconceptions to their work, that's the model we need to be able to do good grading.

[00:30:13] It turns out teachers are still beating the state of the art. Like teachers are so good at this task that if you get a great teacher, uh, they can way outperform a neural network app.

[00:30:24] Russ Altman: And when you say outperform, you mean you say to the teacher, let's assume this student doesn't understand concept X. 

[00:30:31] Chris Piech: Yeah.

[00:30:31] Russ Altman: What will their assignment look like? 

[00:30:33] Chris Piech: Yeah. 

[00:30:33] Russ Altman: They're good at that?

[00:30:34] Chris Piech: Oh, so good at it. Way better than any neural network at the moment. This could change, but at the moment. And I think the future is probably going to look like a hybrid because, you know, teachers will know their students in ways that AI probably never will. Uh, AI will be able to assist because it has, you know, all the power of all the knowledge of all the programs it's ever read. Um, and together, I think we're going to be a fantastic team at understanding students. 

[00:30:55] Russ Altman: It also strikes me that new teachers, you know, in, I'm a physician. One of the things I am is a doctor. And everybody knows that you have to see a few thousand patients before you get really good and you don't, and of course it's stressful to be one of those patients early on because you don't know if you should have confidence in the physician.

[00:31:12] And you can imagine with this generative capability, that teachers might go into a situation now, a new situation, a new job, and they can say, I've graded this a thousand times or a hundred times. And so I am not clueless about how to grade this assignment. 

[00:31:26] Chris Piech: Yeah. And you know, we can imagine that teamwork is both training the teacher, getting great feedback to students, um, and maybe this is the future of education. 

[00:31:37] Russ Altman: Thanks to Chris Piech. That was the future of computer aided education. Thanks for tuning into this episode. With over 250 episodes in our archive, you have instant access to a huge array of discussions on the future of pretty much everything. If you're enjoying the show, a reminder to please consider sharing it with your friends and colleagues.

[00:31:58] Personal recommendations are the best way for us to grow the show. You can connect with me on X or Twitter, @RBAltman. And you can connect with Stanford Engineering @StanfordENG.

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Advantages and Disadvantages of Computer Assisted Instruction

Looking for advantages and disadvantages of Computer Assisted Instruction?

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What is Computer Assisted Instruction?

Computer Assisted Instruction is when computers are used to teach or help students learn. It can include things like educational games, online lessons, or software that makes learning more fun and interactive.

What are the advantages and disadvantages of Computer Assisted Instruction

The following are the advantages and disadvantages of Computer Assisted Instruction:

Advantages of Computer Assisted Instruction

Disadvantages of computer assisted instruction.

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Computer-Aided Engineering Approach to Optimize Hydroformed Exhaust Gas Recirculation Tube under Thermal Load 2024-01-5086

This study emphasizes the importance of computer-aided engineering (CAE) approach in optimizing exhaust gas recirculation (EGR) tube under thermal load. With exhaust gases generating high temperatures, the EGR tube experiences increased stress and strain, posing challenges to its structural integrity. Moreover, the cyclic heating and cooling cycles of the engine imposes thermal fatigue, further compromising the tube’s performance over time. To address these concerns, the paper introduces a comprehensive CAE methodology for conducting factor of safety analysis. The nonlinear thermal analysis is performed on the assembly as due to high temperatures the stresses cross the yield limit. The strain-based approach is used to calculate the factor of safety. Moreover, a comprehensive case study is presented, illustrating how design modifications can enhance the thermal fatigue factor of safety. By adjusting parameters such as thickness and routing, engineers can mitigate thermal stresses and improve the tube’s fatigue life. This approach allows analysts to thoroughly evaluate whether the tube design can endure thermal fatigue for a specified number of cycles. By utilizing advanced simulation tools and techniques, engineers gain valuable insights into the tube’s behavior under varying thermal conditions. This enables informed decision-making regarding design modifications aimed at enhancing durability and reliability. Ultimately, the integration of CAE in the optimization process empowers automotive manufacturers to develop EGR tubes that meet stringent performance requirements while minimizing the risk of premature failure due to thermal stresses and fatigue. The tool used for simulation of the EGR tube assembly is ANSYS 2023R1.

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The Future Of Computer-Assisted Education

googletag.cmd.push(function() { googletag.display('div-gpt-ad-1675415194720-0'); }); It is indeed that education is the most powerful weapon that can change the world. The only difference between a successful and an unsuccessful person is education. Today if we see the consequences of the COVID-19 pandemic if, in such situations, if we do not have computer-based education then the student will not able to be educated. As, all the schools and colleges have been shut down due to COVID-19 and the education is been continuing online we can say that computer-based education is a boon to students. Also, it is not limited to syllabus and classroom; it is much more than that.

Advantages of computer-based education.

1.It is the most simplified and interactive way of gaining knowledge

2.Not limited to the syllabus.

3.Makes learning simplified and motivates students.

4.It is a combination of video, audio, and text.

Methods Of Computer-Assisted Education

Smart classes.

By the use of smart classes’ interaction, process of explanation, and transfer of knowledge become attractive and interactive. It involves using photographs, maps, audio, and graphics along with the teaching process. The use of smart classes will also increase the participation of students in class.

It is the most popular app which is used to conduct meetings and take classes. The pandemic creates havoc in the whole world; this app provides a platform, and support to all. This type of app is the need of the hour. This app proves that there are no boundaries for education and that’s why computer-assisted technologies are booming.

Online Courses

Today, in this modern era where competition is increasing daily, it is essential for people to learn new skills continuously. Having different skills and knowledge along and a degree is a must in the present time.

Computer-Assisted Learning Assessment Tools

Multiple-choice questions.

Computer-based education provides us with a series of multiple-choice questions to enhance our learning process. These questions designed to assess a student’s understanding of things they have taught.

Fill In The Gaps

In this type of question, the students have to fill an answer in the space or gap provided. In some questions, suitable words are also given to the students. These questions designed to test whether a student can remember what he read or not.

Crossword Puzzles

These puzzles specially designed in a manner that supports learning while playing. Students take it as an exciting game. This, besides being a game, supports learning of different concept, words, and spellings. Crossword puzzle also focuses on improving the vocabulary of students.

Listening Exercise

In this exercise, the question is asked from the listener or student and the person who is listening, has to pick correct answer from the given options. This enhances the listening skill of a person. In addition to that this improves the language. These exercises are set in a way that students will gain more and more knowledge.

Does Computer-Based Education Replace The Teacher?

It is the concern of many people that computer-based education will replace teachers and increases the unemployment as, after the introduction of computer based education the students will study and learn from computer. But this statement is not valid. Computer-based education is only a means to assist teachers in teaching and providing additional knowledge to students. Like in this pandemic, various apps such as ZOOM, G-meet help teachers to take online classes; similarly, computer-based education will give support to the teacher.

In the upcoming time, computer-based education will play an essential role in the education system. It will provide more and more knowledge to a student. Moreover, it will not only give knowledge related to subject and course like teachers but, also, help in learning different languages , skills, and many more things. It is not limited to a particular boundary; instead, it is a hub of knowledge.

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"SUCCESS OF COMPUTER ASSISTED EDUCATION"

2020, Inesha & Gopal

The purpose of this research is to explore and dig deeper into the new way of education assisted by computer and its technologies. It throws light on the new innovation of computer assisted education and its success in today's era. Computer aided education refers to using computer and its devices. It does not mean replacing teachers but teachers using computer devices and its applications to teach like smart classes, videos and web tutoring. This paper highlights how in the situation of pandemic like this, we are only relying on the computer assisted education. Without computer facilitated education today, it would have been merely impossible for us to study and attain education. The world has open handedly accepted the new technology of computer assisted education. This paper is done with the objective to observe the success of computer assisted education.

Related Papers

International Journal of Multidisciplinary Educational Research (IJMER)

Pravat Kumar Jena

Covid-19, as a global pandemic, has called for social distancing. It has made people mandatory to sit indoor and sitting idle indoor may lead to mental stress. Hence to keep people engaged and free from mental stress, online learning can play important role. Online learning is the best solution during this pandemic situation. Teachers can use virtual classrooms to teach from home with all necessary tools which makes the online sessions as effective as traditional ones. Pandemics often compel the learners to stay at home for long period of time and obstruct teaching-learning process. This article emphasizes on how online learning is beneficial during times of crises like work absences or pandemics. Therefore, some tools and techniques for online learning which can ensure the continuity of learning are highlighted. Some emerging approaches of Government of India for online learning are presented. Merits and demerits of online learning platform are also discussed. Perceptions of learners and educators on Online Learning system during lockdown are pointed.

Journal of technological innovation and research

انتخاب عالم خان

Corona pandemic (COVID-19) opened up new room for academic discussion in the pedagogic perspectives. It was so abrupt that many institutions were even not aware of the minor or major effect of the disease worldwide. The result was a complete standstill for a couple of days. On the other hand, institutions who have been utilizing blended learning techniques for regular education or distance learning mode gradually shifted to complete virtual classrooms. King Abdulaziz university (KAU), being the most high-tech institution in the entire Middle East/Gulf easily coped with the saddest time for education in the recent history. This research is a case study of a government university in Saudi Arabia (King Abdulaziz university, Jeddah). The university administered each of its faculty in the same way wthout any exception, however policies at the department level(s) were formulated locally. The data and observations used in the study were based on the teaching-learning practices occuring at the faculty of applied studies of KAU. It was found the e-learning/distance learning is very important s a tool of education, however, there are many challenges on the way to successful implementations of educational principles in order to attain predetermined aims and objectives.It was concluded that distance learning is the only way we can continue education in the time of COVID-19 that has disrupted global education scenario. But, proper training for human resource and institutional preparedness is inevitable. It is recommended that an extended empirical and in-depth study be carried out for better inputs and interventions.

International Journal of Scientific and Research Publications

Ankuran Dutta

Indian higher education can boast of being one of the torch bearers among many developing nations for its rich plethora of dynamic content. Due to the Novel Coronavirus (COVID-19) pandemic, in all the higher educational institutions, which includes universities, standalone institutes, and colleges, a total of about 10 million academic hours are compromised, which will be rather difficult to compensate. The University Grants Commission through its advisory instructed all the institutes to continue classes in online mode as per feasibility and engage ICT tools available for use in academic discourse. Many institutions have been using different social media platforms for the dissemination of knowledge. The present crisis has revolutionized the entire higher education architecture of the country through videoconferencing based online learning since there’s no other option to compensate for the compromised academic activities. This paper aims at exploring the kind of social media used to disseminate learning resources to the students, and the impact it crafting on their educational loss. It also elucidates the effectiveness of online classes, e-learning pedagogy, and its outcome through structured qualitative analysis. For Citation: Dr Ankuran Dutta (2020); Impact of Digital Social Media on Indian Higher Education: Alternative Approaches of Online Learning during COVID-19 Pandemic Crisis; International Journal of Scientific and Research Publications (IJSRP) 10(05) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.10.05.2020.p10169

Register Journal

Arif Nugroho

In view of the COVID-19 pandemic and government policy to carry out online learning, the present research is aimed at investigating how EFL teachers carry out online EFL learning and its challenges. 16 EFL teachers volunteered to participate in this research through invitation. The EFL teachers were requested to make written reflections regarding their practices in carrying out online EFL learning and the challenges they encounter. Five of them were involved in a follow-up interview individually. Semi-structured interview was administered. Data coding was done and appropriate extracts were informed in results section. To validate the data, data coding was done independently by Arief Eko Priyo Atmojo, Arif Nugroho 50 both researchers, continued by several cycles of discussion. As results, the EFL teachers have carried out online learning through a series of activities ranging from checking the students' attendance to giving score on the students' works synchronously or asynchronously depending on each school policy. Various applications and platforms ranging from learning management system to additional resource are employed. However, many problems emerge from the students, the teachers, and the students' parents along with the valid reasons. Therefore, the online learning does not run well since it lacks of preparation and planning. Implications for better online learning are discussed. Future prospective researches are directed and encouraged.

International Journal for Innovative Research in Multidisciplinary Field (IJIRMF)

The pandemic Covid-19 came as havoc for developing countries like India. It has significantly disrupted the education sector which is a critical determinant of a country’s economic future. It has compelled the human society to maintain social distancing. It has made people mandatory to sit indoor and sitting idle indoor may lead to mental stress. Hence, it has created more challenges to keep people engaged and free from mental stress. Open and Distance Learning (ODL) system is the best solution to meet the challenges of education during this pandemic situation of COVID-19. Every challenge is an opportunity. These challenges have also created opportunities for the educational institutes to strengthen their technological knowledge and infrastructure to tackle the Covid-19 like situation. Indian education system is more acquainted with face to face or physical teaching learning process. Most of educators and learners are not equipped with use of technology in education and there is also lack of practice and motivation towards use of technology in education which creates more challenges during pandemics. This article highlights different challenges and opportunities created by Covid-19 for ODL system of Indira Gandhi National Open University (IGNOU). The steps taken by IGNOU to meet the challenges by exploring various opportunities are pointed. Some tools and techniques for distance learning which can ensure the continuity of learning during the current pandemic are described. Some suggestions for handling the challenges created by Covid-19 by exploring various opportunities for ODL system are also pointed in the article.

International Journal of Multidisciplinary Sciences and Advanced Technology

Oyeniran Oluwashina

Education is the process by which society deliberately transmits its accumulated knowledge, values, and skills from one generation to another through institutions. A sound educational system is therefore prerequisite to achieving progress, from the individual to the society to the economy. Discontinuity in education is a threat to learning in Nigeria and the effect of repeated closures of schools and academic programs on students' learning has adverse effects on the students, the parents and the nation as a whole. The ongoing discontinuity in education is caused by some global issues that affect almost every continents of the world and as a result led to total lockdown. This discontinuity in education was caused by COVID-19, a newly discovered coronavirus. But using emerging computer-based technology as a resource, students are encouraged to explore their own interests and to become active learners during the lockdown session. Hence, the efficacy of e-learning platforms that will foster continued learning cannot be ignored. Thus, this study proposed E-Learning as advancement in Nigerian pedagogy amid Covid-19 Pandemic lockdown by proposing a method that will put an end to discontinuity in education that emerged as a result of COVID-19 pandemic lockdown. The proposed computer and android applications do not cost the lecturers and the students any more money than data subscription charges from their respective data network providers. Moreover, this framework also allows the institutional management to monitor these academic activities and allow the lecturers to upload the courseware and lecture notes to the E-learning zones and interact with the students while the students will also access the e-learning zones to attend their various classes as scheduled by the lecturers or as directed by the school management. Thus, the e-learning zone serves as the meeting point or lecture room for the students and the lecturers alike.

Journal of Education and Practice

Michael Onyema Edeh, Ph.D

Coronavirus Disease (COVID-19) outbreak poses serious concerns to global education systems. Efforts to contain COVID-19 prompted unscheduled closure of schools in more than 100 countries worldwide. COVID-19 school closures left over one billion learners out of school. The study investigates the impact of COVID-19 on education. Data were collected through structured questionnaires administered to 200 respondents that consist of teachers, students, parents, and policy makers selected from different countries. The collected data were analyzed using STATA/Regression. The results show that COVID-19 has adverse effects on education including, learning disruptions, and decreased access to education and research facilities, Job losses and increased student debts. The findings also show that many educators and students relied on technology to ensure continued learning online during the Coronavirus pandemic. However, online education was hindered by poor infrastructures including, network, power, inaccessibility and unavailability issues and poor digital skills. The study underscores the damaging effects of COVID-19 on education sector and the need for all educational institutions, educators, and learners to adopt technology, and improve their digital skills in line with the emerging global trends and realities in education.

Krishna Ghimire

https://www.ijrrjournal.com/IJRR_Vol.7_Issue.5_May2020/Abstract_IJRR0049.html

International Journal of Research & Review (IJRR)

The coronavirus, which started in Wuhan (China), has spread to developed and underdeveloped countries, with the greatest impact being so far on the developed countries like America, Italy etc. The biggest impact of Lockdown has been on the economy, here we cannot exclude the impact mainly on education, the traditional education that lockdown used to bring to students is now completely shut down somewhere. The impact of lockdown on education has been mentioned in this paper issue, the education issue has become very big and similarly in the future, exams in all schools and colleges in Maharashtra (India) have been cancelled, mainly on traditional education of students and what they think about online education during the lockdown period and its consequences, with the help of a few questions. The information has been collected here this.

sphiwe wezzie , Hejia Wang

This article presents 15 autoethnographical texts detailing student experiences at Beijing Normal University in the midst of the Covid-19 pandemic. Contributions have been collected over 6 weeks between 15 February and 1 April 2020, edited by Hejia Wang (assisted by Moses Oladele Ogunniran and Yingying Huang), and supervised by Michael Peters. Through shared in-depth empirical feelings and representations from a wide variety of cultural, historical, and social contexts, the article outlines an answer to the question: How do students, connected virtually but separated physically in an internationalized university, deal with disruption brought about by the Covid-19 pan-demic? Student testimonies offer reflections on Covid-19 and Chinese international education, experiences of online teaching and learning, reflections on university coping mechanisms, an account of realities and feelings related to changes in academic life, and discussions on coping strategies in Chinese international higher education. Contributors expose their individual feelings, effects, benefits, challenges, and risk management strategies. Collected at the peak of the Covid-19 pandemic, these testimonies are unable to offer systemic answers to challenges facing the whole world. However, these experiences and feelings will provide important inputs to global discussions about the future of the world, after Covid-19.

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